R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/

This software is also referenced in ORMS.

References in zbMATH (referenced in 6475 articles , 6 standard articles )

Showing results 1 to 20 of 6475.
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  1. Sengupta, Debasis; Jammalamadaka, Sreenivasa Rao: Linear models and regression with R. An integrated approach (2020)
  2. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2020)
  3. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  4. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  5. Akinkunmi, Mustapha: Introduction to statistics using R (2019)
  6. Alarcón-Soto, Yovaninna; Langohr, Klaus; Fehér, Csaba; García, Felipe; Gómez, Guadalupe: Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox model (2019)
  7. Alfaro, Esteban (ed.); Gámez, Matías (ed.); García, Noelia (ed.): Ensemble classification methods with applications in R (2019)
  8. Alireza S. Mahani; Mansour T.A. Sharabiani: Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC (2019) not zbMATH
  9. Allévius, Benjamin; Höhle, Michael: An unconditional space-time scan statistic for ZIP-distributed data (2019)
  10. Amalan Mahendran; Pushpakanthie Wijekoon: fitODBOD: An R Package to Model Binomial Outcome Data using Binomial Mixture and Alternate Binomial Distributions (2019) not zbMATH
  11. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  12. Amrei Stammann, Daniel Czarnowske: Binary Choice Models with High-Dimensional Individual and Time Fixed Effects (2019) arXiv
  13. Anderson, David F.; Higham, Desmond J.; Leite, Saul C.; Williams, Ruth J.: On constrained Langevin equations and (bio)chemical reaction networks (2019)
  14. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  15. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  16. Andrew Thomas Jones, Hien Duy Nguyen, Jessica Juanita Bagnall: BoltzMM: an R package for maximum pseudolikelihoodestimation of fully-visible Boltzmann machines (2019) not zbMATH
  17. Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
  18. Anne Petersen; Claus Ekstrøm: dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R (2019) not zbMATH
  19. Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
  20. Antonio Calcagnì, Massimiliano Pastore, Gianmarco Altoè: ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in R (2019) arXiv

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Further publications can be found at: http://journal.r-project.org/